Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this paper, we develop an algorithm called scene analysis for robot planning (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion. Supplementary material can be found at https://tinyurl.com/sarp22

本文提出了一种在不确定情况下，让机器人根据场景分析算法构建的场景图，并利用图像信息进行机器人规划，从而在部分可观察性的领域中实现上下文感知机器人规划。实验结果表明，在目标搜索领域，该算法比标准机器人规划和场景分析方法具有更高的任务完成效率和准确性。

利用场景图进行部分可观测机器人规划的推理